Overview

Dataset statistics

Number of variables9
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory83.7 B

Variable types

Text2
Numeric7

Dataset

Description3월달 물가현황(농산물, 축산물, 수산물, 생필품, 외식 등 28가지 기준 물품의 가격을 동별로 확인하여 정리해서 올림 예: 사과, 소고기, 명태, 쌀 등)입니다.
Author부산광역시 해운대구
URLhttps://www.data.go.kr/data/15063783/fileData.do

Alerts

우동(센텀홈플러스) is highly overall correlated with 중동(이마트 해운대점) and 5 other fieldsHigh correlation
중동(이마트 해운대점) is highly overall correlated with 우동(센텀홈플러스) and 5 other fieldsHigh correlation
좌동(GS수퍼마켓) is highly overall correlated with 우동(센텀홈플러스) and 5 other fieldsHigh correlation
송정동 is highly overall correlated with 우동(센텀홈플러스) and 5 other fieldsHigh correlation
반여2동(골목시장) is highly overall correlated with 우동(센텀홈플러스) and 5 other fieldsHigh correlation
반송동(탑마트) is highly overall correlated with 우동(센텀홈플러스) and 5 other fieldsHigh correlation
재송동(한마음시장) is highly overall correlated with 우동(센텀홈플러스) and 5 other fieldsHigh correlation
품 목 has unique valuesUnique
중동(이마트 해운대점) has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:02:30.553180
Analysis finished2024-04-06 08:02:41.296781
Duration10.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품 목
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-04-06T17:02:41.549466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.7857143
Min length1

Characters and Unicode

Total characters106
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row사과
2nd row배
3rd row배추
4th row무
5th row대파
ValueCountFrequency (%)
사과 1
 
3.1%
배 1
 
3.1%
돼지갈비(외식 1
 
3.1%
맥주(외식 1
 
3.1%
소주(외식 1
 
3.1%
맥주(소매점 1
 
3.1%
소주(소매점 1
 
3.1%
식용유 1
 
3.1%
밀가루 1
 
3.1%
두부 1
 
3.1%
Other values (22) 22
68.8%
2024-04-06T17:02:42.186434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 8
 
7.5%
( 8
 
7.5%
고 6
 
5.7%
소 6
 
5.7%
식 5
 
4.7%
5
 
4.7%
기 5
 
4.7%
주 4
 
3.8%
외 4
 
3.8%
파 2
 
1.9%
Other values (43) 53
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85
80.2%
Close Punctuation 8
 
7.5%
Open Punctuation 8
 
7.5%
Space Separator 5
 
4.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
고 6
 
7.1%
소 6
 
7.1%
식 5
 
5.9%
기 5
 
5.9%
주 4
 
4.7%
외 4
 
4.7%
파 2
 
2.4%
가 2
 
2.4%
맥 2
 
2.4%
갈 2
 
2.4%
Other values (40) 47
55.3%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85
80.2%
Common 21
 
19.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
고 6
 
7.1%
소 6
 
7.1%
식 5
 
5.9%
기 5
 
5.9%
주 4
 
4.7%
외 4
 
4.7%
파 2
 
2.4%
가 2
 
2.4%
맥 2
 
2.4%
갈 2
 
2.4%
Other values (40) 47
55.3%
Common
ValueCountFrequency (%)
) 8
38.1%
( 8
38.1%
5
23.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85
80.2%
ASCII 21
 
19.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 8
38.1%
( 8
38.1%
5
23.8%
Hangul
ValueCountFrequency (%)
고 6
 
7.1%
소 6
 
7.1%
식 5
 
5.9%
기 5
 
5.9%
주 4
 
4.7%
외 4
 
4.7%
파 2
 
2.4%
가 2
 
2.4%
맥 2
 
2.4%
갈 2
 
2.4%
Other values (40) 47
55.3%

규 격
Text

Distinct22
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-04-06T17:02:42.655284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length12
Mean length9.3928571
Min length2

Characters and Unicode

Total characters263
Distinct characters69
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)57.1%

Sample

1st row부사(1개 300g이상)3kg
2nd row신고 6㎏
3rd row1㎏
4th row1㎏
5th row1㎏(상품)
ValueCountFrequency (%)
1병 4
 
7.3%
500g 4
 
7.3%
200g 2
 
3.6%
시원소주 2
 
3.6%
정도 2
 
3.6%
1㎏ 2
 
3.6%
0.1㎏ 2
 
3.6%
360㎖ 2
 
3.6%
1마리(냉동 2
 
3.6%
상등육 2
 
3.6%
Other values (28) 31
56.4%
2024-04-06T17:02:43.398908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 29
 
11.0%
28
 
10.6%
1 19
 
7.2%
5 10
 
3.8%
㎏ 10
 
3.8%
2 9
 
3.4%
g 9
 
3.4%
정 7
 
2.7%
) 7
 
2.7%
( 7
 
2.7%
Other values (59) 128
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 107
40.7%
Decimal Number 78
29.7%
Space Separator 28
 
10.6%
Other Symbol 19
 
7.2%
Lowercase Letter 11
 
4.2%
Close Punctuation 7
 
2.7%
Open Punctuation 7
 
2.7%
Other Punctuation 5
 
1.9%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 7
 
6.5%
도 6
 
5.6%
리 5
 
4.7%
마 5
 
4.7%
등 5
 
4.7%
상 4
 
3.7%
동 4
 
3.7%
냉 4
 
3.7%
병 4
 
3.7%
부 3
 
2.8%
Other values (40) 60
56.1%
Decimal Number
ValueCountFrequency (%)
0 29
37.2%
1 19
24.4%
5 10
 
12.8%
2 9
 
11.5%
6 4
 
5.1%
3 4
 
5.1%
4 2
 
2.6%
8 1
 
1.3%
Other Symbol
ValueCountFrequency (%)
㎏ 10
52.6%
㎝ 5
26.3%
㎖ 4
 
21.1%
Lowercase Letter
ValueCountFrequency (%)
g 9
81.8%
k 1
 
9.1%
ℓ 1
 
9.1%
Space Separator
ValueCountFrequency (%)
28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 146
55.5%
Hangul 107
40.7%
Latin 10
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 7
 
6.5%
도 6
 
5.6%
리 5
 
4.7%
마 5
 
4.7%
등 5
 
4.7%
상 4
 
3.7%
동 4
 
3.7%
냉 4
 
3.7%
병 4
 
3.7%
부 3
 
2.8%
Other values (40) 60
56.1%
Common
ValueCountFrequency (%)
0 29
19.9%
28
19.2%
1 19
13.0%
5 10
 
6.8%
㎏ 10
 
6.8%
2 9
 
6.2%
) 7
 
4.8%
( 7
 
4.8%
. 5
 
3.4%
㎝ 5
 
3.4%
Other values (7) 17
11.6%
Latin
ValueCountFrequency (%)
g 9
90.0%
k 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136
51.7%
Hangul 107
40.7%
CJK Compat 19
 
7.2%
Letterlike Symbols 1
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29
21.3%
28
20.6%
1 19
14.0%
5 10
 
7.4%
2 9
 
6.6%
g 9
 
6.6%
) 7
 
5.1%
( 7
 
5.1%
. 5
 
3.7%
6 4
 
2.9%
Other values (5) 9
 
6.6%
CJK Compat
ValueCountFrequency (%)
㎏ 10
52.6%
㎝ 5
26.3%
㎖ 4
 
21.1%
Hangul
ValueCountFrequency (%)
정 7
 
6.5%
도 6
 
5.6%
리 5
 
4.7%
마 5
 
4.7%
등 5
 
4.7%
상 4
 
3.7%
동 4
 
3.7%
냉 4
 
3.7%
병 4
 
3.7%
부 3
 
2.8%
Other values (40) 60
56.1%
Letterlike Symbols
ValueCountFrequency (%)
ℓ 1
100.0%

우동(센텀홈플러스)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12139.286
Minimum990
Maximum72000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-06T17:02:43.724061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum990
5-th percentile1502
Q13267.5
median4800
Q313000
95-th percentile44325
Maximum72000
Range71010
Interquartile range (IQR)9732.5

Descriptive statistics

Standard deviation16913.756
Coefficient of variation (CV)1.3933074
Kurtosis5.401859
Mean12139.286
Median Absolute Deviation (MAD)2660
Skewness2.3502998
Sum339900
Variance2.8607516 × 108
MonotonicityNot monotonic
2024-04-06T17:02:44.460491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
13000 2
 
7.1%
5000 2
 
7.1%
41400 1
 
3.6%
2900 1
 
3.6%
1710 1
 
3.6%
1390 1
 
3.6%
10800 1
 
3.6%
4600 1
 
3.6%
2290 1
 
3.6%
4300 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
990 1
3.6%
1390 1
3.6%
1710 1
3.6%
1990 1
3.6%
2290 1
3.6%
2450 1
3.6%
2900 1
3.6%
3390 1
3.6%
3500 1
3.6%
3840 1
3.6%
ValueCountFrequency (%)
72000 1
3.6%
45900 1
3.6%
41400 1
3.6%
38950 1
3.6%
18500 1
3.6%
13950 1
3.6%
13000 2
7.1%
10800 1
3.6%
7990 1
3.6%
6580 1
3.6%

중동(이마트 해운대점)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13176.786
Minimum1170
Maximum49935
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-06T17:02:44.714830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1170
5-th percentile1617.75
Q14365
median5965
Q316260
95-th percentile49165
Maximum49935
Range48765
Interquartile range (IQR)11895

Descriptive statistics

Standard deviation15157.483
Coefficient of variation (CV)1.1503172
Kurtosis1.6093085
Mean13176.786
Median Absolute Deviation (MAD)3900
Skewness1.6651859
Sum368950
Variance2.2974928 × 108
MonotonicityNot monotonic
2024-04-06T17:02:44.959633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
38900 1
 
3.6%
49900 1
 
3.6%
23000 1
 
3.6%
18000 1
 
3.6%
6000 1
 
3.6%
5000 1
 
3.6%
1660 1
 
3.6%
1170 1
 
3.6%
11450 1
 
3.6%
5950 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1170 1
3.6%
1595 1
3.6%
1660 1
3.6%
1780 1
3.6%
2540 1
3.6%
2780 1
3.6%
3990 1
3.6%
4490 1
3.6%
4580 1
3.6%
4980 1
3.6%
ValueCountFrequency (%)
49935 1
3.6%
49900 1
3.6%
47800 1
3.6%
38900 1
3.6%
23000 1
3.6%
21500 1
3.6%
18000 1
3.6%
15680 1
3.6%
11850 1
3.6%
11450 1
3.6%

좌동(GS수퍼마켓)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15888.929
Minimum1410
Maximum83000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-06T17:02:45.179371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1410
5-th percentile1655
Q14430
median6750
Q316100
95-th percentile63830
Maximum83000
Range81590
Interquartile range (IQR)11670

Descriptive statistics

Standard deviation21340.91
Coefficient of variation (CV)1.3431308
Kurtosis3.6277237
Mean15888.929
Median Absolute Deviation (MAD)4030
Skewness2.0894998
Sum444890
Variance4.5543443 × 108
MonotonicityNot monotonic
2024-04-06T17:02:45.451356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
5000 2
 
7.1%
4980 2
 
7.1%
1980 2
 
7.1%
44500 1
 
3.6%
8500 1
 
3.6%
17000 1
 
3.6%
10500 1
 
3.6%
1410 1
 
3.6%
9780 1
 
3.6%
4580 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
1410 1
3.6%
1480 1
3.6%
1980 2
7.1%
2390 1
3.6%
2900 1
3.6%
3980 1
3.6%
4580 1
3.6%
4900 1
3.6%
4950 1
3.6%
4980 2
7.1%
ValueCountFrequency (%)
83000 1
3.6%
66000 1
3.6%
59800 1
3.6%
44500 1
3.6%
25000 1
3.6%
24800 1
3.6%
17000 1
3.6%
15800 1
3.6%
10960 1
3.6%
10500 1
3.6%

송정동
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12093.976
Minimum798
Maximum69000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-06T17:02:45.778751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum798
5-th percentile1575
Q13795
median6750
Q314600
95-th percentile40480.5
Maximum69000
Range68202
Interquartile range (IQR)10805

Descriptive statistics

Standard deviation15131.689
Coefficient of variation (CV)1.2511757
Kurtosis7.7433513
Mean12093.976
Median Absolute Deviation (MAD)4200
Skewness2.6484104
Sum338631.33
Variance2.2896803 × 108
MonotonicityNot monotonic
2024-04-06T17:02:46.061987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3980.0 3
 
10.7%
8500.0 2
 
7.1%
10900.0 2
 
7.1%
23190.0 1
 
3.6%
798.0 1
 
3.6%
15400.0 1
 
3.6%
22000.0 1
 
3.6%
5000.0 1
 
3.6%
4000.0 1
 
3.6%
1900.0 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
798.0 1
 
3.6%
1400.0 1
 
3.6%
1900.0 1
 
3.6%
1980.0 1
 
3.6%
2500.0 1
 
3.6%
2980.0 1
 
3.6%
3480.0 1
 
3.6%
3900.0 1
 
3.6%
3980.0 3
10.7%
4000.0 1
 
3.6%
ValueCountFrequency (%)
69000.0 1
3.6%
49500.0 1
3.6%
23730.0 1
3.6%
23190.0 1
3.6%
22000.0 1
3.6%
15400.0 1
3.6%
14900.0 1
3.6%
14500.0 1
3.6%
12900.0 1
3.6%
10900.0 2
7.1%

반여2동(골목시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10476.786
Minimum1250
Maximum56000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-06T17:02:46.342540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1250
5-th percentile1517.5
Q14000
median5950
Q311925
95-th percentile36150
Maximum56000
Range54750
Interquartile range (IQR)7925

Descriptive statistics

Standard deviation12458.334
Coefficient of variation (CV)1.1891371
Kurtosis6.6692856
Mean10476.786
Median Absolute Deviation (MAD)3000
Skewness2.5142849
Sum293350
Variance1.5521009 × 108
MonotonicityNot monotonic
2024-04-06T17:02:46.529860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5000 3
 
10.7%
15000 2
 
7.1%
4000 2
 
7.1%
7000 2
 
7.1%
3000 1
 
3.6%
8000 1
 
3.6%
9000 1
 
3.6%
1850 1
 
3.6%
1550 1
 
3.6%
8200 1
 
3.6%
Other values (13) 13
46.4%
ValueCountFrequency (%)
1250 1
 
3.6%
1500 1
 
3.6%
1550 1
 
3.6%
1850 1
 
3.6%
2500 1
 
3.6%
3000 1
 
3.6%
4000 2
7.1%
4200 1
 
3.6%
4500 1
 
3.6%
5000 3
10.7%
ValueCountFrequency (%)
56000 1
3.6%
40000 1
3.6%
29000 1
3.6%
20000 1
3.6%
15000 2
7.1%
12000 1
3.6%
11900 1
3.6%
9000 1
3.6%
8200 1
3.6%
8000 1
3.6%

반송동(탑마트)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11538.571
Minimum1380
Maximum52800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-06T17:02:46.791909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1380
5-th percentile1428.5
Q14000
median5325
Q311600
95-th percentile40300
Maximum52800
Range51420
Interquartile range (IQR)7600

Descriptive statistics

Standard deviation13399.529
Coefficient of variation (CV)1.1612815
Kurtosis3.0771509
Mean11538.571
Median Absolute Deviation (MAD)3695
Skewness1.9538799
Sum323080
Variance1.7954739 × 108
MonotonicityNot monotonic
2024-04-06T17:02:47.088427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4000 2
 
7.1%
39000 1
 
3.6%
41000 1
 
3.6%
11500 1
 
3.6%
11000 1
 
3.6%
1590 1
 
3.6%
1500 1
 
3.6%
8980 1
 
3.6%
5350 1
 
3.6%
4550 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
1380 1
3.6%
1390 1
3.6%
1500 1
3.6%
1590 1
3.6%
3180 1
3.6%
3980 1
3.6%
4000 2
7.1%
4200 1
3.6%
4280 1
3.6%
4500 1
3.6%
ValueCountFrequency (%)
52800 1
3.6%
41000 1
3.6%
39000 1
3.6%
32500 1
3.6%
18500 1
3.6%
16400 1
3.6%
11900 1
3.6%
11500 1
3.6%
11000 1
3.6%
10000 1
3.6%

재송동(한마음시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10092.143
Minimum1000
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-06T17:02:47.413738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1328
Q13000
median5050
Q313500
95-th percentile35275
Maximum50000
Range49000
Interquartile range (IQR)10500

Descriptive statistics

Standard deviation11868.83
Coefficient of variation (CV)1.1760466
Kurtosis5.6119192
Mean10092.143
Median Absolute Deviation (MAD)3610
Skewness2.3164781
Sum282580
Variance1.4086914 × 108
MonotonicityNot monotonic
2024-04-06T17:02:47.661575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4000 2
 
7.1%
15000 2
 
7.1%
3900 2
 
7.1%
3000 2
 
7.1%
20000 1
 
3.6%
9000 1
 
3.6%
10000 1
 
3.6%
1800 1
 
3.6%
1380 1
 
3.6%
10800 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
1000 1
3.6%
1300 1
3.6%
1380 1
3.6%
1500 1
3.6%
1600 1
3.6%
1800 1
3.6%
3000 2
7.1%
3800 1
3.6%
3900 2
7.1%
4000 2
7.1%
ValueCountFrequency (%)
50000 1
3.6%
43500 1
3.6%
20000 1
3.6%
19000 1
3.6%
17500 1
3.6%
15000 2
7.1%
13000 1
3.6%
10800 1
3.6%
10000 1
3.6%
9000 1
3.6%

Interactions

2024-04-06T17:02:39.727505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:31.100770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:32.778477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:33.984632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:35.617343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:36.953159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:38.529417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:39.903671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:31.281911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:32.954093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:34.168707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:35.818940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:37.151408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:38.734838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:40.057865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:31.444504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:33.133155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:34.411143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:35.968954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:37.362779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:38.888766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:40.249454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:31.646181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:33.334227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:34.671761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:36.133988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:37.638987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:39.082433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:40.401195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:32.186693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:33.510163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:34.904269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:36.302130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:37.846549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:39.252972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:40.554198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:32.388066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:33.666436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:35.128376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:36.463680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:38.040096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:39.408346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:40.701227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:32.560062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:33.834615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:35.386761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:36.640328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:38.278804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:39.580051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:02:47.834319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품 목규 격우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
품 목1.0001.0001.0001.0001.0001.0001.0001.0001.000
규 격1.0001.0000.8310.6600.8310.5870.9800.8970.687
우동(센텀홈플러스)1.0000.8311.0000.7680.9650.9490.8780.8580.950
중동(이마트 해운대점)1.0000.6600.7681.0000.8280.7970.8580.8940.785
좌동(GS수퍼마켓)1.0000.8310.9650.8281.0000.9280.8930.8810.946
송정동1.0000.5870.9490.7970.9281.0000.8580.9310.993
반여2동(골목시장)1.0000.9800.8780.8580.8930.8581.0000.9850.892
반송동(탑마트)1.0000.8970.8580.8940.8810.9310.9851.0000.966
재송동(한마음시장)1.0000.6870.9500.7850.9460.9930.8920.9661.000
2024-04-06T17:02:48.092363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
우동(센텀홈플러스)1.0000.9240.8540.9280.7660.8790.908
중동(이마트 해운대점)0.9241.0000.8850.9450.8480.9500.943
좌동(GS수퍼마켓)0.8540.8851.0000.8940.8140.8520.891
송정동0.9280.9450.8941.0000.8430.9010.938
반여2동(골목시장)0.7660.8480.8140.8431.0000.8940.901
반송동(탑마트)0.8790.9500.8520.9010.8941.0000.946
재송동(한마음시장)0.9080.9430.8910.9380.9010.9461.000

Missing values

2024-04-06T17:02:40.906657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:02:41.157396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

품 목규 격우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
0사과부사(1개 300g이상)3kg41400389004450023190.0150003900020000
1배신고 6㎏72000478006600023730.0290004100019000
2배추1㎏3840449039804933.333333450039801600
3무1㎏1990178014801980.0150013801300
4대파1㎏(상품)3390254099403980.0250031803800
5소고기(국산)등심 상등육 500g38950499358300049500.0400003250043500
6소고기(수입)등심 상등육 500g18500215002500014500.0119001640015000
7돼지고기삼겹살 500g13950118501580014900.0120001190013000
8닭고기육계1㎏79907480109608500.0700075007500
9달 걀특란 10개3990399049803980.0420042003900
품 목규 격우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
18고춧가루0.1㎏4300590049502980.0650053003900
19두부500g 판두부(포장두부 420g) 1모2290548045802500.0125045501500
20밀가루백설표 중력분1등2.5㎏4600595049808500.0540053504600
21식용유백설표옥수수기름1.8ℓ1080011450978012900.08200898010800
22소주(소매점)시원소주 360㎖ 1병1390117014101400.0155015001380
23맥주(소매점)하이트 500㎖ 1병1710166019801900.0185015901800
24소주(외식)시원소주 360㎖ 1병5000500050004000.0400040004000
25맥주(외식)하이트 500㎖ 1병5000600050005000.0400040004000
26돼지갈비(외식)200g 정도13000180001050022000.090001100010000
27삼겹살(외식)200g 정도13000230001700015400.080001150015000